Wound management and treatment using computer vision and machine learning

ABSTRACT

Certain aspects of the present disclosure provide techniques for wound management and treatment. This includes determining characteristics of a wound for a patient based on an image of the wound, including detecting the characteristics based on analyzing the image using a first ML model trained to detect wound characteristics from a captured image. The techniques further include identifying patient medical data including characteristics relating to a medical history for the patient, and predicting a first care plan for the patient based on providing the characteristics of the wound and the patient medical data to a second ML model. The second ML model is trained to predict the first care plan using prior wound care outcome data including a prior wound care outcomes relating to prior patients. The first care plan is configured to be used to treat the wound for the patient.

INTRODUCTION

Aspects of the present disclosure relate to artificial intelligence andhealthcare, and more specifically, to improved wound management andtreatment using computer vision and machine learning (ML).

Managing and treating wounds in a patient is a common healthcare goal.For example, a patient may sustain a wound, and may seek treatment forthe wound in a healthcare facility, or the patient may sustain, orworsen, a wound while residing in a healthcare facility or receivingmanaged care in an outpatient facility. Managing and treating thesewounds is difficult because different wounds can require differenttreatment and can take different amounts of time to heal, depending oncharacteristics of the wound and of the patient. Further, generating acare plan for treatment is typically done manually by a care provider.This requires in-person examination and assessment of the wound, andmanual creation, or modification, of a care plan to treat the wound. Butthis approach is prone to inaccuracies, for example because of thepotential for human error, and is inefficient, because it requires handson assessment and care plan generation by a care provider.

SUMMARY

Certain embodiments provide a method. The method includes determining aplurality of characteristics of a wound for a patient based on an imageof the wound, including: detecting the plurality of characteristicsbased on analyzing the image using a first machine learning (ML) modeltrained to detect wound characteristics from a captured image. Themethod further includes identifying patient medical data including aplurality of characteristics relating to a medical history for thepatient. The method further includes predicting a first care plan forthe patient based on providing the plurality of characteristics of thewound and the patient medical data to a second ML model. The second MLmodel is trained to predict the first care plan using prior wound careoutcome data including a plurality of prior wound care outcomes relatingto a plurality of prior patients, and the first care plan is configuredto be used to treat the wound for the patient.

Further embodiments provide an apparatus including a memory, and ahardware processor communicatively coupled to the memory, the hardwareprocessor configured to perform operations. The operations includedetermining a plurality of characteristics of a wound for a patientbased on an image of the wound, including: detecting the plurality ofcharacteristics based on analyzing the image using a ML model trained todetect wound characteristics from a captured image. The operationsfurther include identifying patient medical data including a pluralityof characteristics relating to a medical history for the patient. Theoperations further include predicting a first care plan for the patientbased on providing the plurality of characteristics of the wound and thepatient medical data to a second ML model. The second ML model istrained to predict the first care plan using prior wound care outcomedata including a plurality of prior wound care outcomes relating to aplurality of prior patients, and the first care plan is configured to beused to treat the wound for the patient.

Further embodiments provide a non-transitory computer-readable mediumincluding instructions that, when executed by a processor, cause theprocessor to perform operations. The operations include determining aplurality of characteristics of a wound for a patient based on an imageof the wound, including: detecting the plurality of characteristicsbased on analyzing the image using a ML model trained to detect woundcharacteristics from a captured image. The operations further includeidentifying patient medical data including a plurality ofcharacteristics relating to a medical history for the patient. Theoperations further include predicting a first care plan for the patientbased on providing the plurality of characteristics of the wound and thepatient medical data to a second ML model. The second ML model istrained to predict the first care plan using prior wound care outcomedata including a plurality of prior wound care outcomes relating to aplurality of prior patients, and the first care plan is configured to beused to treat the wound for the patient.

The following description and the related drawings set forth in detailcertain illustrative features of one or more embodiments.

DESCRIPTION OF THE DRAWINGS

The appended figures depict certain aspects of the one or moreembodiments and are therefore not to be considered limiting of the scopeof this disclosure.

FIG. 1 depicts a computing environment for wound management andtreatment using computer vision and ML, according to one embodiment.

FIG. 2 depicts a block diagram for a prediction controller for woundmanagement and treatment using computer vision and ML, according to oneembodiment.

FIG. 3 is a flowchart illustrating wound management and treatment usingcomputer vision and ML, according to one embodiment.

FIG. 4 illustrates detecting wound characteristics from a captured imageusing computer vision, according to one embodiment.

FIG. 5 depicts an example of detecting wound characteristics from acaptured image using computer vision, according to one embodiment.

FIG. 6 is a flowchart illustrating training a computer vision ML modelfor wound management and treatment, according to one embodiment.

FIG. 7 depicts predicting a wound care plan using an ML model, accordingto one embodiment.

FIG. 8 depicts wound characteristics for use in predicting a wound careplan using an ML model, according to one embodiment.

FIG. 9 depicts patient characteristics for use in predicting a woundcare plan using an ML model, according to one embodiment.

FIG. 10 depicts patient medical history for use in predicting a woundcare plan using an ML model, according to one embodiment.

FIG. 11 depicts historical wound care incident data for use inpredicting a wound care plan using an ML model, according to oneembodiment.

FIG. 12 is a flowchart illustrating training an ML model for woundmanagement and treatment using computer vision, according to oneembodiment.

FIG. 13 depicts using a wound care plan generated using an ML model,according to one embodiment.

FIG. 14 depicts ongoing monitoring of patient care for wound managementand treatment using computer vision, according to one embodiment.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe drawings. It is contemplated that elements and features of oneembodiment may be beneficially incorporated in other embodiments withoutfurther recitation.

DETAILED DESCRIPTION

Aspects of the present disclosure provide apparatuses, methods,processing systems, and non-transitory computer-readable mediums forimproved wound management and treatment using computer vision and ML. Asdiscussed above, a patient wound is typically treated using a care planoutlining various treatment tasks. This care plan, in existing practice,is commonly created manually by a care provider after examining thewound (e.g., in person). But this is inefficient, because it requiresmanual intervention, and it can be ineffective, because it is subject tohuman error and variance between care professionals. Alternatively, inexisting practice, the care plan may be created using a pre-definedrubric or algorithm with pre-defined rules. This is also inefficient,because it requires a very large number of pre-defined rules andsignificant manual oversight, and ineffective because using the specificrubric or algorithm is extremely unlikely to be effective for allpatients and all wounds. Accordingly, existing practices may generallylead to inconsistent and ineffective patient care outcomes.

In aspects described herein, a care plan for treating a patient woundcan instead be created automatically using a trained ML model, based ona captured image of the wound or other captured sensor data. Forexample, a patient or care provider can capture an image of a patientwound. Computer vision techniques (e.g., a suitable ML model, asdiscussed further below) can be used to analyze the image and detectvarious characteristics of the wound from the image. A suitableprediction ML model (e.g., a deep learning neural network (DNN)) can betrained to predict a care plan for the patient wound, based on thedetected wound characteristics and additional information about thepatient. For example, the prediction ML model can use patientcharacteristics (e.g., demographic information, medication information,and assessment information) and patient medical history (e.g., priormedical conditions and treatments for the patient), along with thedetected wound characteristics, to predict a care plan for the wound.This care plan can outline a set of treatment tasks to follow intreating the wound. Beneficially, this can provide both technicaladvantages and advantages in treating a patient. For example, asdiscussed further below, this can provide technical advantages overconventional techniques in the healthcare field by reducing computationburden and shifting computational burden from prediction time (whencomputational resources may be tied up and results are likely to be timesensitive) to an earlier training phase (when computational resourcescan be scheduled and are likely to be more freely available). Further,as also discussed further below, this provides treatment benefits to thepatient by providing a more accurate and consistent care plan, allowingfor prophylactic early treatment when high priority issues areidentified, and allowing for rapid adjustment of a care plan based onreal-time monitoring.

In an embodiment, the prediction ML model can be trained to predict acare plan using data about historical wound care incidents. For example,the prediction ML model can receive data about prior patient wounds,including characteristics of the relevant patient and wound, the careplan used, the facility used, and the resolution of the treatment. Asnoted above, this data can be used to train the ML model to predict acare plan for a newly identified wound, based on characteristics of thewound (e.g., detected from an image using computer vision techniques)and the patient.

Further, the patient can be continuously monitored during treatment ofthe wound (e.g., automatically using suitable sensors or manually bycare providers), and the prediction ML model can update the predictedcare plan based on the monitoring data. For example, additional imagesof the wound can be captured during treatment, computer visiontechniques can be used to detect characteristics of the wound as it istreated, from the captured images, and the prediction ML model canpredict a revised care plan for the wound using the updatedcharacteristics. Further, the progress of the treatment of the wound canbe used to continuously train the prediction ML model to improve futurepredictions.

Thus, aspects described herein provide significant advantages comparedto conventional approaches for generating care plans. For example,predicting a care plan for treating a patient wound automatically usinga trained ML model, based on a captured image of the wound or othercaptured sensor data, provides for an accurate care plan whileminimizing the needed computational resources for the prediction andshifting the computational burden from prediction time (e.g., when nearreal-time response may be needed) to an earlier training time (e.g.,when resources can be easily dedicated to the training). In anembodiment, generating a care plan using a specific rubric or algorithmwith pre-defined rules can be computationally expensive, because a verylarge number of rules are needed and parsing and following the rules iscomputationally expensive. Further, this computationally expensiveanalysis is done at the time the care plan is generated, when a rapidresponse is likely to be needed (e.g., so that the patient can betreated quickly).

Predicting a care plan for treating a patient wound automatically usinga trained ML model, by contrast, is significantly less computationallyexpensive at the time the care plan is generated. For example, theprediction ML model can be trained up-front during a training phase,when rapid response is not necessary and computational resources arereadily available. The trained ML model can then be used to rapidly, andcomputationally relatively cheaply, predict a care plan for the patient.This provides a significant technical advantage over prior techniques byshifting the computational burden from the prediction time, when a rapidresponse is needed and computational resources may be engaged in othertasks, to a planned training time when a rapid response is not necessaryand computational resources are available.

As another example, predicting a care plan for treating a patient woundautomatically using a trained ML model, based on a captured image of thewound or other captured sensor data, provides for a more accurate andwell-defined prediction. In an embodiment, a care plan for a wound canbe manually created by a care provider. But this leaves the risk ofhuman error and allows for significant variances among humanpractitioners, which can result in a lack of certainty in the accuracyof the care plan. Predicting the care plan using a trained ML model canboth lessen the risk of human error, and provide more certainty in thelevel of accuracy of the care plan. Further, the predicted care plan canitself be reviewed and refined by a care provider. This provides astarting point for the care provider with a more certain level ofaccuracy, and reduces the burden on the care provider to generate thecare plan themselves.

Example Computing Environment

FIG. 1 depicts a computing environment 100 for wound management andtreatment using computer vision and ML, according to one embodiment. Inan embodiment, a captured wound image 102 is provided to a detectionlayer 110. For example, a patient may have a wound (e.g., bedsores,sutures, abrasions, lesions, or any other visible wound) that isdetectable using an image capture device. The patient, a healthcare, acaretaker, or any other person can capture an image of the wound usingthe image capture device (e.g., a digital camera). For example, apatient or healthcare professional can use a camera integrated into asmartphone or tablet computer to capture the wound image 102, and canuse a suitable secure application to provide the image to the detectionlayer 110. This is merely one example, and any suitable image capturedevice can be used by any suitable person, or entity, to capture thewound image 102. For example, an automated sensor could be used toautomatically trigger image capture of the wound image 102 (e.g., duringa medical examination). Further, the image capture device can operateoutside the visual spectrum (e.g., an infrared sensor, an x-ray sensor,or any other suitable sensor).

In an embodiment, the captured wound image 102 is provided to thedetection layer 110 using a suitable communication network. For example,the wound image 102 can be captured using a camera in a computing device(e.g., a smartphone or tablet computer camera) and can be transferred tothe detection layer using the computing device. The computing device canuse any suitable communication network, including the Internet, a widearea network, a local area network, or a cellular network, and can useany suitable wired or wireless communication technique (e.g., WiFi orcellular communication). This is merely one example, and the wound image102 can be captured by a camera and provided to a computing device usingany suitable technique (e.g., using storage medium or through a wired orwireless transmission from the camera to the computing device).

The detection layer 110 includes a wound detection service 112, whichincludes a wound detection ML model 114. In an embodiment, the wounddetection service 112 facilitates transformation of incoming patientdata (e.g., wound image 102). For example, as discussed below withregard to FIG. 2 , the wound detection service 112 can be computersoftware service implemented in a suitable controller (e.g., theprediction controller 200 illustrated in FIG. 2 ) or combination ofcontrollers. In an embodiment the detection layer 110, and the wounddetection service 112, can be implemented using any suitable combinationof physical compute systems, cloud compute nodes and storage locations,or any other suitable implementation. For example, the detection layer110 could be implemented using a server or cluster of servers. Asanother example, the detection layer 110 can be implemented using acombination of compute nodes and storage locations in a suitable cloudenvironment. For example, one or more of the components of the detectionlayer 110 can be implemented using a public cloud, a private cloud, ahybrid cloud, or any other suitable implementation.

As one example, the wound detection service 112 can facilitate computervision analysis of the wound image 102. In this example, the wounddetection ML model 114 can be a suitable computer vision ML model (e.g.,a DNN, support vector machine (SVM), or any other suitable ML model). Inan embodiment, the wound detection ML model 114 can be trained toreceive the wound image 102, and to recognize or detect variouscharacteristics of the wound depicted in the image. These can includeexterior characteristics (e.g., size and color), interiorcharacteristics (e.g., size, color, and depth), location, and any othersuitable characteristics. This is discussed further below with regard toFIGS. 4-5 and 8 .

In an embodiment, the wound image 102 is merely one example of patientdata that can be analyzed using the detection layer 110 (e.g., using thewound detection service 112 and the wound detection ML model 114). Forexample, captured sensor data 104 can also be provided to the detectionlayer 110. In an embodiment, the captured sensor data 104 includes datacaptured by sensors used during treatment or rehabilitation of a patient(e.g., captured during treatment of a wound). For example, the capturedsensor data 104 can include data from negative pressure wound therapydevices, oxygen and intubation devices, monitored pressure and drainagedevices, or any other suitable devices.

In an embodiment, the wound detection service 112 can further facilitateanalysis of the captured sensor data 104. For example, the wounddetection service 112 can use a wound detection ML model 114 to detectand identify characteristics of the patient's wound based on thecaptured sensor data. In an embodiment, the wound detection ML model 114can be any suitable ML model (e.g., a DNN, a decision tree, a randomforest, a support vector machine, and other ML model types) trained todetect and identify characteristics of the patient's wound.

Further, in an embodiment, the wound detection ML model 114 can includemultiple ML models trained to detect wound characteristics fromdifferent data. For example, one ML model could be trained to usecomputer vision techniques to identify wound characteristics from thewound image 102, another ML model could be trained to detect woundcharacteristics based on sensor data from a wound therapy device, andanother ML model could be trained to detect wound characteristics basedon sensor data from monitored pressure devices. In some aspects, thesedifferent models may be ensemble to produce a prediction. This is merelyan example, and the wound detection ML model could instead be trained touse data from multiple sources (e.g., the wound image 102 and capturedsensor data 104), together, to detect and identify characteristics ofthe patient's wound.

In an embodiment, the detection layer 110 provides wound detection datato a prediction layer 120. For example, the wound detection service 112can use the wound detection ML model 114 to detect characteristics of apatient wound, using the wound image 102, the captured sensor data 104,or both. The detection layer 110 can provide these wound characteristicsto the prediction layer 120.

The prediction layer 120 includes a wound prediction service 122 and awound prediction ML model 124. In an embodiment, the wound predictionservice 122 facilitates prediction of treatment and rehabilitationinformation for the patient wound. For example, the wound predictionservice 122 can use the wound prediction ML model 124 to determine atreatment prediction 150 (e.g., a wound care plan) and predict any othersuitable treatment and rehabilitation information for the patient wound.This is discussed further below with regard to FIG. 7 .

As discussed below with regard to FIG. 2 , the wound prediction service122 can be computer software service implemented in a suitablecontroller (e.g., the prediction controller 200 illustrated in FIG. 2 )or combination of controllers. In an embodiment the prediction layer120, and the wound prediction service 122, can be implemented using anysuitable combination of physical compute systems, cloud compute nodesand storage locations, or any other suitable implementation. Forexample, the prediction layer 120 could be implemented using a server orcluster of servers. As another example, the prediction layer 120 can beimplemented using a combination of compute nodes and storage locationsin a suitable cloud environment. For example, one or more of thecomponents of the prediction layer 120 can be implemented using a publiccloud, a private cloud, a hybrid cloud, or any other suitableimplementation.

As discussed above, the prediction layer 120 uses the detectedcharacteristics of the patient wound (e.g., the output from thedetection layer 110) to predict the treatment and rehabilitationinformation for the patient wound. In an embodiment, however, the woundcharacteristics detected by the detection layer 110 are not sufficientto allow the prediction layer 120 to accurately predict the treatmentand rehabilitation information for the patient wound. For example,merely identifying the characteristics of the wound may not besufficient to identify a suitable treatment plan for the patient, andmay not be sufficient to identify a suitable treatment facility for thepatient.

In an embodiment, the prediction layer 120 can further receive, and use,patient medical data 130 and historical wound care data 140. Forexample, the patient medical data 130 can include patientcharacteristics 132 and patient medical history 134. In an embodiment,the patient characteristics 132 can include patient demographics (e.g.,age, height, weight), patient medications (e.g., a listing ofmedications for the patient), patient assessment data (e.g., intakeassessment data, discharge assessment data, activities of daily living(ADL) assessment data), or any other suitable patient characteristics.This is discussed further below with regard to FIG. 9 . In anembodiment, the patient medical history 134 can include medicalcondition data (e.g., diagnosis, onset, treatment, and resolution) forany prior medical conditions. This is discussed further below withregard to FIG. 10 .

In an embodiment, the historical wound care data 140 can include dataabout in-patient outcomes 142 and outpatient outcomes 144, for variouspatients and various wounds. For example, the historical wound care data140 can include wound characteristics for a wound (e.g., exteriorcharacteristics, interior characteristics, and location), patientcharacteristics for the patient with the wound (e.g., demographics,medications, assessments, and medical history), care plan history forthe wound (e.g., treatments used), facility characteristics fortreatment of the wound (e.g., type of facility, staffing at thefacility, and available resources at the facility), resolution data(e.g., time and resources used in treatment, and result of thetreatment), and any other suitable historical wound care data. In anembodiment, the patient medical data 130 provides data about theparticular patient with the wound, while the historical wound care data140 provides data about historical treatments and resolutions for avariety of wounds and patients. Further, in an embodiment, thehistorical wound care data 140 has had any personally identifyingpatient information removed.

In an embodiment, the patient medical data 130 and the historical woundcare data 140 are provided to the prediction layer 120 using a suitablecommunication network. For example, the patient medical data 130 and thehistorical wound care data 140 can be stored in one or more suitableelectronic databases (e.g., a relational database, a graph database, orany other suitable database) or other electronic repositories (e.g., acloud storage location, an on-premises network storage location, or anyother suitable electronic repository). The patient medical data 130 andthe historical wound care data 140 can be provided from the respectiveelectronic repositories to the prediction layer 120 using any suitablecommunication network, including the Internet, a wide area network, alocal area network, or a cellular network, and can use any suitablewired or wireless communication technique (e.g., WiFi or cellularcommunication).

As discussed above, in an embodiment, the wound prediction service 122uses the wound prediction ML model 124 to predict treatment andrehabilitation information for the patient wound. For example, the woundprediction ML model 124 can be a suitable supervised ML model (e.g., aDNN) trained to generate a treatment prediction 150 for the patientwound from a combination of wound characteristics for the particularwound at issue (e.g., output from the detection layer 110), patientmedical data 130, and historical wound care data 140. This is discussedfurther below with regard to FIG. 3 . For example, the wound predictionML model 124 can be selected based on initial analysis of the input data(e.g., the wound characteristics, patient medical data 130, andhistorical wound care data 140). In an embodiment, a basic technique canbe initially selected (e.g., logistic regression), data can be convertedto a numerical format, and based on initial analysis data transformationand ML techniques can be chosen. This is merely an example, and anysuitable supervised, or unsupervised, techniques can be used.

For example, the wound prediction ML model can predict a care plan forthe wound, including recommended treatments and medications. This is oneexample of a treatment prediction 150. In an embodiment, the care plan(or any other suitable treatment prediction 150) can be provided to atreatment facility 160. In an embodiment, the treatment facility 160 canbe any suitable in-patient or out-patient treatment facility. Further,in an embodiment, the care plan can be provided directly to the patientor to the patient's medical care provider. This is discussed furtherbelow with regard to FIG. 13 . In an embodiment, the treatmentprediction 150 is provided to any, or all of the treatment facility, thepatient, and the care provider using a suitable communication network.For example, the treatment prediction 150 can be provided from theprediction layer 120 to the destination (e.g., treatment facility,patient, or care provider) using any suitable communication network,including the Internet, a wide area network, a local area network, or acellular network, and can use any suitable wired or wirelesscommunication technique (e.g., WiFi or cellular communication).

In an embodiment, the treatment prediction 150 is used to treat thepatient. For example, the treatment prediction 150 can be a wound careplan provided to the treatment facility 160. Care providers at thetreatment facility 160, or the patient them self, can use the wound careplan to treat the wound (e.g., using the identified treatments andmedications). In an embodiment, the treatment of the wound can bemonitored, and ongoing patient monitoring data 170 can be gathered. Forexample, repeated images of the wound can be captured, other sensor datacan be provided, care providers can provide assessment data, and anyother suitable data can be gathered. Further, in an embodiment, captureddata can be maintained in suitable repository (e.g., an electronicdatabase) and used for training (e.g., training the wound prediction MLmodel 124). This data, and all training data, can be stripped of anypersonally identifying patient information.

In an embodiment, this ongoing patient monitoring data 170 can beprovided to the detection layer 110, the prediction layer 120, or both,and used to refine the treatment prediction 150. For example, capturedimages or other captured sensor data can be provided to the detectionlayer 110 and analyzed in the same way as the wound image 102 and thecaptured sensor data 104 (e.g., to identify ongoing woundcharacteristics as the wound is treated). As another example, updatedpatient medical data can be provided to the prediction layer 120 andanalyzed in the same way as the patient medical data 130.

Further, in an embodiment, the ongoing patient monitoring data 170 canbe used to continuously train the wound prediction ML model 124. Forexample, the wound prediction ML model 124 can determine, from theongoing patient monitoring data 170 (e.g., from detected woundcharacteristics of additional captured images of the wound as it istreated), whether the wound is progressing in treatment and how quicklyit is progressing. As one example, the color, shape, size, condition(e.g., oozing or dry), or depth of the wound may change duringtreatment, indicating progress in healing. The wound prediction service122 can use the prior predicted care plan, and the result of the care asindicated by the ongoing patient monitoring data, as additional trainingdata to further train the wound prediction ML model 124 to predict acare plan that provides successful treatment to patients.

FIG. 2 depicts a block diagram for a prediction controller 200 for woundmanagement and treatment using computer vision and ML, according to oneembodiment. The controller 200 includes a processor 202, a memory 210,and network components 220. The memory 210 may take the form of anynon-transitory computer-readable medium. The processor 202 generallyretrieves and executes programming instructions stored in the memory210. The processor 202 is representative of a single central processingunit (CPU), multiple CPUs, a single CPU having multiple processingcores, graphics processing units (GPUs) having multiple execution paths,and the like.

The network components 220 include the components necessary for thecontroller 200 to interface with a suitable communication network (e.g.,a communication network interconnecting various components of thecomputing environment 100 illustrated in FIG. 1 , or interconnecting thecomputing environment 100 with other computing systems). For example,the network components 220 can include wired, WiFi, or cellular networkinterface components and associated software. Although the memory 210 isshown as a single entity, the memory 210 may include one or more memorydevices having blocks of memory associated with physical addresses, suchas random access memory (RAM), read only memory (ROM), flash memory, orother types of volatile and/or non-volatile memory.

The memory 210 generally includes program code for performing variousfunctions related to use of the prediction controller 200. The programcode is generally described as various functional “applications” or“modules” within the memory 210, although alternate implementations mayhave different functions and/or combinations of functions. Within thememory 210, the wound detection service 112 facilitates detecting woundcharacteristics from captured sensor data (e.g., captured images andother captured sensor data), using the wound detection ML model 114.This is discussed further below with regard to FIGS. 4-6 . The woundprediction service 122 facilitates predicting treatment andrehabilitation information for a wound, using the wound prediction MLmodel 124. This is discussed further below with regard to FIGS. 3 and 7.

While the controller 200 is illustrated as a single entity, in anembodiment, the various components can be implemented using any suitablecombination of physical compute systems, cloud compute nodes and storagelocations, or any other suitable implementation. For example, thecontroller 200 could be implemented using a server or cluster ofservers. As another example, the controller 200 can be implemented usinga combination of compute nodes and storage locations in a suitable cloudenvironment. For example, one or more of the components of thecontroller 200 can be implemented using a public cloud, a private cloud,a hybrid cloud, or any other suitable implementation.

Although FIG. 2 depicts the wound detection service 112, the woundprediction service 122, the wound detection ML model 114, and the woundprediction ML model 124, as being mutually co-located in memory 210,that representation is also merely provided as an illustration forclarity. More generally, the controller 200 may include one or morecomputing platforms, such as computer servers for example, which may beco-located, or may form an interactively linked but distributed system,such as a cloud-based system, for instance. As a result, processor 202and memory 210 may correspond to distributed processor and memoryresources within the computing environment 100. Thus, it is to beunderstood that any, or all, of the wound detection service 112, thewound prediction service 122, the wound detection ML model 114, and thewound prediction ML model 124 may be stored remotely from one anotherwithin the distributed memory resources of the computing environment100.

FIG. 3 is a flowchart 300 illustrating wound management and treatmentusing computer vision and ML, according to one embodiment. At block 302a wound detection service (e.g., the wound detection service 112illustrated in FIGS. 1-2 ) receives captured sensor data relating to apatient wound. For example, as discussed above in relation to FIG. 1 ,in an embodiment the wound detection service can receive a capturedwound image (e.g., the wound image 102 illustrated in FIG. 1 ), capturedsensor data (e.g., the captured sensor data 104 illustrated in FIG. 1 ),or both.

At block 304, the wound detection service detects wound characteristicsfrom the captured data using an ML model. For example, the wounddetection service can use a captured image, sensor data, or both todetect exterior characteristics (e.g., size and color), interiorcharacteristics (e.g., size, color, and depth), location, and any othersuitable characteristics of the wound. As discussed above in relation tothe wound detection ML model 114 illustrated in FIG. 1 , the wounddetection service can use any suitable ML model, or combination of MLmodels, to detect wound characteristics from the captured sensor data.This is discussed further below with regard to FIGS. 4-6 .

At block 306, a prediction service (e.g., the wound prediction service122 illustrated in FIGS. 1-2 ) receives patient medical data. Forexample, the prediction service can receive the patient medical data 130illustrated in FIG. 1 . This can include patient characteristics (e.g.,patient demographics, patient medications, patient assessment data, orany other suitable patient characteristics) and patient medical history(e.g., medical condition data for any prior medical conditions). This isdiscussed further below with regard to FIGS. 9-10 .

At block 308, the prediction service receives historical wound caredata. For example, the prediction service can receive the historicalwound care data 140 illustrated in FIG. 1 . This can include historicaldata about in-patient outcomes and outpatient outcomes, for variouspatients and various wounds. This is discussed further below with regardto FIG. 11 . In an embodiment, the prediction service uses thehistorical wound care data for ongoing training of the prediction MLmodel. Alternatively, the prediction service does not receive thehistorical wound care data. In this example, the historical wound caredata is used to train the prediction ML model (e.g., as discussed belowin relation to FIG. 12 ) but is not used for inference (e.g., forprediction).

At block 310, the prediction service predicts a care plan for thepatient wound using an ML model. For example, the prediction service canuse the wound prediction ML model 124 illustrated in FIGS. 1-2 topredict the care plan. The prediction ML model can be any suitable MLmodel trained to use the wound characteristics (e.g., detected fromcaptured sensor data using an ML model at block 304), the patientmedical data received at block 306, and the historical wound care datareceived at block 308, to predict a care plan for the patient wound.This can include predicting treatments, medications, and any othersuitable care for the patient wound. This is discussed further belowwith regard to FIG. 7 .

As illustrated the prediction ML model uses all of the woundcharacteristics, the patient medical data, and the historical wound caredata, to predict the care plan. But this is merely an example.Alternatively, or in addition, the prediction ML model can use anysubset of this data (e.g., where some of this data is unavailable for agiven patient wound). For example, the prediction ML model can use thewound characteristics and patient medical data, without historical woundcare data, or wound characteristics and historical wound care data,without patient medical data. In an embodiment this may result in aslight loss of accuracy in predicting the care plan, but the predictedcare plan is still significantly improved over prior techniques (e.g.,manual creation of the care plan).

In an embodiment, the prediction service can further identify aprophylactic treatment task for the wound (e.g., a treatment taskintended to quickly prevent further disease or issues with the wound).For example, the prediction service can use the wound characteristics,the patient medical data, including but not limited to specific healthrelated data associated with one or more patients, such as age, weight,medical conditions, demographics, or other such data, or both toidentify a high priority treatment task (e.g. a medication, bandaging,or another medical procedure) needed for the wound (e.g., bedsores,sutures, abrasions, lesions, or any other wound). As one example, awound could be identified as requiring immediate medical treatment(e.g., bandaging, a surgical procedure, a particular medication, or anyother suitable treatment), to prevent further disease or issues with thewound. Thus, for example, a bedsore, suture, abrasion, or lesion couldbe identified as requiring immediate medication, immediate bandaging, oranother immediate medical procedure. The prediction service can transmitan alert (e.g., an e-mail, SMS message, telephone call, or another formof electronic message) describing the treatment task to a care providerfor the patient (e.g., via a care facility for the patient) or to thepatient themselves. The care provider or patient can then treat thewound using the treatment task. In an embodiment, the prediction servicecan identify this treatment task prior to completing the prediction ofthe care plan. For example, the prediction service can identify a highpriority treatment task while predicting the care plan, and can transmitthe alert prior to completing the prediction of the care plan. In anembodiment this allows for a rapid alert for the treatment task, withoutwaiting for complete prediction of the care plan.

At block 312, the prediction service receives ongoing data formtreatment monitoring. For example, the prediction service can receiveadditional sensor data (e.g., additional images) captured duringtreatment and rehabilitation of the patient wound. This data can becaptured at a treatment facility (e.g., an in-patient or out-patientfacility), by a suitable medical professional or by the patient themself. In an embodiment, the prediction service can use the ongoing datato further refine the wound care plan.

Example of Detecting Wound Characteristics from a Captured Image

FIG. 4 illustrates detecting wound characteristics from a captured imageusing computer vision, according to one embodiment. In an embodiment,FIG. 4 provides one example of detecting wound characteristics fromcaptured data using an ML model, discussed above in relation to block304 illustrated in FIG. 3 . A wound image 102 (e.g., as discussed abovein relation to FIG. 1 ) is provided to a computer vision service 410 anda computer vision ML model 412. In an embodiment, the wound image 102 isan image of the patient wound captured using any suitable image capturedevice (e.g., a camera, a medical imaging device, or any other suitableimage capture device).

In an embodiment, the computer vision service 410 is one example of awound detection service 112, and the computer vision ML model 412 is oneexample of a wound detection ML model 114, both illustrated in FIGS. 1-2. As discussed above, in an embodiment the wound detection service 112can detect wound characteristics from a variety of captured sensor data,including a captured image or captured sensor data from treatmentdevices, using the wound detection ML model. The computer vision service410 detects wound characteristics 420 from the wound image 102 using thecomputer vision ML model 412.

In an embodiment, the computer vision ML model 412 can be any suitableML model. For example, a non-neural network ML model can be used (e.g.,a SVM). This can use any suitable object detection, recognition, oridentification technique. As another example, a neural network ML modelcan be used (e.g., a CNN), and can use any suitable object detection,recognition, or identification technique.

As discussed above, the wound characteristics 420 can include anysuitable wound characteristics. These can include exteriorcharacteristics (e.g., size and color), interior characteristics (e.g.,size, color, and depth), location, and any other suitablecharacteristics. This is discussed further below with regard to FIG. 8 .

FIG. 5 depicts an example of detecting wound characteristics from acaptured image using computer vision, according to one embodiment. In anembodiment, a captured image depicts a wound on a patient. As discussedabove, a suitable wound detection service (e.g., the computer visionservice 410 illustrated in FIG. 4 ) detects characteristics of the woundfrom the image, using a suitable wound detection ML model (e.g., thecomputer vision ML model 412 illustrated in FIG. 4 ). For example, thewound detection service can detect an exterior size 502 and an exteriorcolor 508. As another example, the wound detection service can detect aninterior size and color 506, and a depth 504.

Example of Training a Computer Vision ML Model

FIG. 6 is a flowchart 600 illustrating training a computer vision MLmodel for wound management and treatment, according to one embodiment.This is merely an example, and in an embodiment a suitable unsupervisedtechnique could be used (e.g., without requiring training). At block602, a training service (e.g., a human administrator or a software orhardware service) collects historical wound image data. For example, awound detection service (e.g., the wound detection service 112illustrated in FIGS. 1 and 2 ) can be configured to act as the trainingservice and collect previously captured images of patient wounds (e.g.,gathered over time). This is merely an example, and any suitablesoftware or hardware service can be used (e.g., a wound detectiontraining service).

At block 606, the training service (or other suitable service)pre-processes the collected historical wound image data. For example,the training service can create feature vectors reflecting the values ofvarious features, for each collected wound image. At block 608, thetraining service receives the feature vectors and uses them to train atrained computer vision ML model 412 (e.g., the computer vision model412 illustrated in FIG. 4 ).

In an embodiment, at block 604 the training service also collectsadditional wound data (e.g., data generated from in-person evaluation ofthe wound). At block 606, the training service can also pre-process thisadditional wound data. For example, the feature vectors corresponding tothe historical wound image data can be further annotated using theadditional wound data. Alternatively, or in addition, additional featurevectors corresponding to the additional wound data can be created. Atblock 608, the training service uses the pre-processed additional wounddata during training to generate the trained computer vision ML model412.

In an embodiment, the pre-processing and training can be done as batchtraining. In this embodiment, all data is pre-processed at once (e.g.,all historical wound image data and additional wound data), and providedto the training service at block 608. Alternatively, the pre-processingand training can be done in a streaming manner. In this embodiment, thedata is streaming, and is continuously pre-processed and provided to thetraining service. For example, it can be desirable to take a streamingapproach for scalability. The set of training data may be very large, soit may be desirable to pre-process the data, and provide it to thetraining service, in a streaming manner (e.g., to avoid computation andstorage limitations). Further, in an embodiment, a federated learningapproach could be used in which multiple healthcare entities contributeto training a shared model.

Example of Predicting a Wound Care Plan

FIG. 7 depicts predicting a wound care plan using an ML model, accordingto one embodiment. In an embodiment, FIG. 7 corresponds with block 310illustrated in FIG. 3 , above. A wound prediction service 122, asdiscussed above in relation to FIGS. 1-2 , is associated with a careplan prediction ML model 712. In an embodiment, the care plan predictionML model 712 is one example of a wound prediction ML model (e.g., oneexample of the wound prediction ML model 124 illustrated in FIGS. 1-2 ).For example, as illustrated the wound prediction service 122 uses thecare plan prediction ML model 712 to predict a wound care plan 720.

In an embodiment, the wound prediction service 122 uses multiple typesof data to predict the wound care plan 720, using the care planprediction ML model 712. For example, the wound prediction service 122can use detected wound characteristics 702. In an embodiment, thedetected wound characteristics 702 are generated by a wound detectionservice (e.g., the wound detection service 112 illustrated in FIGS. 1-2) using a wound detection ML model (e.g., the wound detection ML model114 illustrated in FIGS. 1-2 ) by detecting wound characteristics fromcaptured data (e.g., a wound image 102, captured sensor data 104, orboth). For example, as illustrated in FIG. 4 , a computer vision service410 can use a computer vision ML model 412 to detect woundcharacteristics 420 from a wound image 102. As discussed below inrelation to FIG. 8 , in an embodiment the detected wound characteristics702 can include exterior characteristics (e.g., size, color), interiorcharacteristics (e.g., size, color, depth), location, and any othersuitable characteristics.

In addition, the wound prediction service 122 can use patientcharacteristics 132 (e.g., as discussed above in relation to FIG. 1 ) topredict the wound care plan 720, using the care plan prediction ML model712. As discussed below in relation to FIG. 9 , the patientcharacteristics 132 can include patient demographics (e.g., age, height,weight), patient medications (e.g., a listing of medications for thepatient), patient assessment data (e.g., intake assessment data,discharge assessment data, activities of daily living (ADL) assessmentdata), or any other suitable patient characteristics.

Further, the wound prediction service 122 can use a patient medicalhistory 134 (e.g., as discussed above in relation to FIG. 1 ) to predictthe wound care plan 720, using the care plan prediction ML model 712. Asdiscussed below in relation to FIG. 10 , the patient medical history 134can include medical condition data (e.g., diagnosis, onset, treatment,and resolution) for any prior medical conditions.

The wound prediction service 122 can further use historical wound caredata 140 (e.g., as discussed above in relation to FIG. 1 ) to predictthe wound care plan 720, using the care plan prediction ML model 712. Asdiscussed below in relation to FIG. 11 , the historical wound care data140 can include wound characteristics for a wound (e.g., exteriorcharacteristics, interior characteristics, and location), patientcharacteristics for the patient with the wound (e.g., demographics,medications, assessments, and medical history), care plan history forthe wound (e.g., treatments used), facility characteristics fortreatment of the wound (e.g., type of facility, staffing at thefacility, and available resources at the facility), resolution data(e.g., time and resources used in treatment, and result of thetreatment), and any other suitable historical wound care data. Asdiscussed above in relation to FIG. 1 , in an embodiment the patientcharacteristics 132 and patient medical history 134 provide data aboutthe particular patient with the wound, while the historical wound caredata 140 provides data about historical treatments and resolutions for avariety of wounds and patients.

In an embodiment, the wound prediction service 122 uses the historicalwound care data 140 for ongoing training of the care prediction ML model712. For example, because training the care prediction ML model 712 maybe computationally expensive, the wound prediction service can train thecare prediction ML model 712 at suitable intervals (e.g., hourly, daily,weekly) or based on triggering events (e.g., after a threshold number ofnew observations are received, upon request from an administrator, or atany other suitable interval). Alternatively, the wound predictionservice 122 does not receive the historical wound care data 140. In thisexample, the historical wound care data 140 is used to train theprediction ML model (e.g., as discussed below in relation to FIG. 12 )but is not used for inference (e.g., for prediction of the wound careplan 720).

In an embodiment, the wound care plan 720 provides a treatment plan fortreating the patient wound. For example, the wound care plan 720 caninclude a set of tasks (e.g., medication tasks, treatment tasks,rehabilitation tasks, physical training tasks, or any other suitabletasks) for the patient, the patient's healthcare provider, the patient'scaretaker, or other assisting personnel, to perform. The wound care plan720 can be predicted by the care plan prediction ML model 712 so thatadhering to the wound care plan 720 will provide optimal, or preferred,treatment to the patient. As discussed above, a care plan is typicallygenerated manually (e.g., by a healthcare provider) or programmaticallyusing a specific rubric or algorithm. This can be inefficient (e.g.,because it requires manual intervention) and ineffective. In anembodiment, the wound care plan 720 generated using the care planprediction ML model 712 provides both effective and efficient treatment.Further, in an embodiment, a healthcare provider can review thegenerated wound care plan 720 and provide any suitable revisions. Thiscan still greatly improve efficiency and effectiveness in creating thecare plan, by assisting the healthcare provider.

In an embodiment, the wound care plan 720 can include treatment tasksrelating to actions to be taken by the patient. For example, the woundcare plan 720 can include information related to preferred nutrition forthe patient. In this example, the patient's compliance with thepreferred nutrition can further be identified during treatment (e.g.,using sensors available at the location where the patient is beingtreated). As another example, the wound care plan 720 can includeinformation about a preferred humidity level to treat the patient'swound. The humidity level at the patient's living facility can bemonitored (e.g., using suitable sensors) and the patient can beencouraged, or assisted, in maintaining a preferred humidity level fortreatment of the wound.

As another example, the wound care plan 720 can include sleep treatmenttasks. For example, the wound care plan 720 can outline amounts of sleepand sleep positions. In this example, a patient with a wound on aparticular location on their body (e.g., a pressure sore) could betreated by describing a sleep position, duration, or both for thepatient to assist in treating the wound (e.g., a position or durationrelieving the pressure on the wound). The patient's sleep could bemonitored and the patient could be assisted with complying with thesleep treatment task. For example, one or more sensors (e.g., includingsmart wearable devices, smart sleep devices, image capture sensors, orany other suitable sensors) could monitor the patient while sleeping andidentify when the patient is not in REM sleep. If the patient issleeping in a position that is not recommended for treating thepatient's wound, the patient could be awakened when not in REM sleep andencouraged, or assisted, to move to a treatment position for furthersleep.

Example Wound and Patient Characteristics

FIG. 8 depicts example wound characteristics 800 for use in predicting awound care plan using an ML model, according to one embodiment. In anembodiment, the wound characteristics 800 provide examples for thedetected wound characteristics 702, illustrated in FIG. 7 and generatedusing a suitable wound detection ML model to detect characteristics fromcaptured wound data (e.g., a captured wound image). For example, thewound characteristics 800 can include one or more wounds 802.

In an embodiment, each wound 802 includes exterior characteristics 810.The exterior characteristics 810 include size 812. For example, the size812 can describe the exterior size of the wound 802 (e.g., the size ofan area surrounding an open area of the wound or surrounding a moreseverely injured portion of the wound). In an embodiment, the size 812can be described in area (e.g., mm²), dimensions, perimetercircumference, or using any other suitable technique. For example, thesize 812 can be expressed as a function describing the exterior size ofthe wound.

The exterior characteristics 810 can further include a color 814. Forexample, the color 814 can describe a color of the exterior portion ofthe wound. The color 814 can be an average color over the exterior area,a most extreme color over the exterior area (e.g., a darkest color,lightest color, color including the largest fraction of a particularshade, etc.), or any other suitable color. Further, the color 814 can beexpressed using a numerical value, a tuple (e.g., a red, green, blue(RGB) value), a textual label, or using any other suitable technique.

In an embodiment, the exterior characteristics can further include aregularity 816 (e.g., a regularity of the shape of the wound), and acondition 818 (e.g., a condition of the exterior of the wound). Forexample, the condition 818 can describe whether the wound is dry orweeping, whether it is sutured or stapled, or any other suitablecondition. These are merely examples, and the exterior characteristics810 can include any suitable characteristics.

In an embodiment, the wound 802 further includes interiorcharacteristics 820. The interior characteristics 820 include a size822. For example, the size 812 can describe the interior size of thewound (e.g., the size of an open area of the wound or of a more severelyinjured portion of the wound). In an embodiment, the size 822 can bedescribed in area (e.g., mm²), dimensions, perimeter circumference, orusing any other suitable technique. For example, the size 822 can beexpressed as a function describing the interior size of the wound.

The interior characteristics 820 can further include a color 824. Forexample, the color 824 can describe a color of the interior portion ofthe wound. The color 824 can be an average color over the interior area,a most extreme color over the interior area (e.g., a darkest color,lightest color, color including the largest fraction of a particularshade, etc.), or any other suitable color. Further, the color 824 can beexpressed using a numerical value, a tuple (e.g., a red, green, blue(RGB) value), a textual label, or using any other suitable technique.

The interior characteristics 820 can further include a depth 826. Forexample, the depth 826 can describe a depth of the wound. This caninclude a tissue depth for an open, or closed, wound, and can beexpressed using a measurement (e.g., mm), relative to a surface portionof the skin, using a label, or using any other suitable technique. Theseare merely examples, and the interior characteristics 820 can includeany suitable characteristics.

In an embodiment, the interior characteristics can further include aregularity 828 (e.g., a regularity of the shape of the wound), and acondition 830 (e.g., a condition of the interior of the wound). Forexample, the condition 830 can describe whether the wound is dry orweeping, whether it is sutured or stapled, or any other suitablecondition.

In an embodiment, the wound 802 further includes a location 840. Forexample, the location 840 can describe the location of the wound on thepatient's body. In an embodiment, the location 840 can be describedrelative to a portion of the patient's body, using a measurement system,or using any other suitable technique. The exterior characteristics 810,interior characteristics 820, and location 840 are merely examples, andthe wound 802 can include any suitable characteristics, organized in anysuitable manner.

FIG. 9 depicts patient characteristics 900 for use in predicting a woundcare plan using an ML model, according to one embodiment. In anembodiment, the wound characteristics 900 provide examples for thepatient characteristics 132, described above in relation to FIG. 1 . Apatient 902 includes patient demographics 910. For example, the patientdemographics 910 can include age 912, height 914, and weight 916. Theseare merely examples, and the patient demographics 910 can include anysuitable characteristics.

The patient 902 can further include patient medications 920. In anembodiment, the patient medications 920 include one or more medications922A-N. These are merely examples, and the patient medications 920 caninclude any suitable data.

Further, the patient 902 can include one or more patient assessments 930(e.g., a patient assessment 930 corresponding to each healthcarefacility to which the patient has been admitted). In an embodiment, thepatient assessment 930 includes an intake assessment 932. For example,an intake assessment can be performed for the patient upon intake to ahealthcare facility (e.g., performed by a suitable healthcareprofessional, using a suitable automated assessment system, or both).The intake assessment can be memorialized as the intake assessment 932.

In an embodiment, the patient assessment 930 further includes adischarge assessment 934. For example, a discharge assessment can beperformed for the patient upon discharge from a healthcare facility(e.g., performed by a suitable healthcare professional, using a suitableautomated assessment system, or both). The discharge assessment can bememorialized as the discharge assessment 934.

The patient assessment 930 can further include an activities of dailyliving (ADL) assessment 936. For example, the ADL assessment canmemorialize the patient's ability to dress, feed, ambulate, toilet, andperform their own hygiene. The ADL assessment can be memorialized as theADL assessment 936. These are merely examples, and the patientassessment 930 can include any suitable data. Further, the patientdemographics 910, patient medications 920, and patient assessment 930are merely examples. The patient 902 can include any suitable patientdata, organized in any suitable fashion.

FIG. 10 depicts patient medical history 1000 for use in predicting awound care plan using an ML model, according to one embodiment. In anembodiment, the patient medical history 1000 provide examples for thepatient medical history 134, described above in relation to FIG. 1 .

A patient 1002 includes one or more medical conditions 1010A-N. Eachmedical condition includes a respective diagnosis 1012A-N, a respectiveonset description 1014A-N (e.g., a date or textual description), arespective treatment 1016A-N (e.g., a treatment history for the medicalcondition), and a respective resolution 1018A-N (e.g., a date ofresolution or a notation that the medical condition is ongoing). Theseare merely examples, and each medical condition 1010A-N can include anysuitable data. Further, the medical conditions 1010A-N are merelyexamples, and the patient 1002 can include any suitable medical historydata.

FIG. 11 depicts historical wound care incident data 1100 for use inpredicting a wound care plan using an ML model, according to oneembodiment. In an embodiment, the historical wound care incident data1100 provide examples for the historical wound care data 140, describedabove in relation to FIG. 1 . Further, in an embodiment, the historicalwound care incident data 1100 corresponds to any suitable patient (e.g.,in addition to the patient for whom a wound is being treated). Forexample, the historical wound care incident data 1100 can be maintainedby a healthcare provider (e.g., in a suitable anonymized or privateformat).

A historical wound care incident 1102 includes patient characteristics1110. In an embodiment, the patient characteristics 1110 correspond withthe patient characteristics 900 illustrated in FIG. 9 (e.g., for thepatient with the historical wound). The patient characteristics 1110include demographics 1112 (e.g., age, height, weight) and medicalhistory 1114. These are merely examples, and the patient characteristics1110 can include any suitable data.

The historical wound care incident 1102 further includes woundcharacteristics 1120. In an embodiment, the wound characteristics 1120correspond with the wound characteristics 800 illustrated in FIG. 8(e.g., for the relevant historical wound). The wound characteristics1120 include exterior characteristics 1122 (e.g., size, color), interiorcharacteristics 1124 (e.g., size, color, depth), and location 1126.These are merely examples, and the wound characteristics 1120 caninclude any suitable data.

The historical wound care incident 1102 further includes care planhistory 1130. For example, the care plan history 1130 can describe oneor more treatments 1132A-N used to treat the relevant wound. These aremerely examples, and the care plan history 1130 can include any suitabledata.

The historical wound care incident 1102 further includes one or morefacility characteristics 1140 (e.g., describing any facilities used totreatment the wound, including outpatient and inpatient facilities). Thefacility characteristics 1140 include a type 1142 (e.g., inpatient,outpatient, or any other suitable type), staffing data 1144 (e.g.,describing a number and type of staffing at the facility), and resourcesdata 1146 (e.g., describing the available resources, includingequipment, staffing, medication, and any other suitable resources).These are merely examples, and the facility characteristics 1140 caninclude any suitable data.

The historical wound care incident 1102 further includes a resolution1150. For example, the resolution 1150 can include a time 1152 (e.g., atime of resolution), resources 1154 (e.g., equipment, staffing, andother resources used in resolution), and result 1156 (e.g., the endresult of treatment). These are merely examples, and the resolution 1150can include any suitable data. Further, the patient characteristics1110, wound characteristics 1120, care plan history 1130, facilitycharacteristics 1140, and resolution 1150, are merely examples. Thehistorical wound care incident 1102 can include any suitable data.

Example of Training an ML Model for Predicting a Wound Care Plan

FIG. 12 is a flowchart 1200 illustrating training an ML model for woundmanagement and treatment using computer vision, according to oneembodiment.

At block 1202, a training service (e.g., a human administrator or asoftware or hardware service) collects historical wound care data. Forexample, a wound prediction service (e.g., the wound prediction service122 illustrated in FIGS. 1 and 2 ) can be configured to act as thetraining service and collect historical wound care data. This is merelyan example, and any suitable software or hardware service can be used(e.g., a wound prediction training service).

At block 1204, the training service (or other suitable service)pre-processes the collected historical wound care data. For example, thetraining service can create feature vectors reflecting the values ofvarious features, for each historical wound.

At block 1206, the training service receives the feature vectors anduses them to train a trained a care plan prediction ML model 712 (e.g.,as discussed above in relation to FIG. 7 ).

In an embodiment, the pre-processing and training can be done as batchtraining. In this embodiment, all data is pre-processed at once (e.g.,all historical wound image data and additional wound data), and providedto the training service at 1206. Alternatively, the pre-processing andtraining can be done in a streaming manner. In this embodiment, the datais streaming, and is continuously pre-processed and provided to thetraining service. For example, it can be desirable to take a streamingapproach for scalability. The set of training data may be very large, soit may be desirable to pre-process the data, and provide it to thetraining service, in a streaming manner (e.g., to avoid computation andstorage limitations).

Example of Using a Predicted Wound Care Plan

FIG. 13 depicts using a wound care plan generated using an ML model,according to one embodiment. In an embodiment, a prediction controller1310 (e.g., the prediction controller 200 illustrated in FIG. 2 )generates a predicted care plan 1320. For example, as discussed above inrelation to block 310 in FIG. 3 and FIG. 7 , a wound prediction service(e.g., the wound prediction service 122 illustrated in FIGS. 1-2 ) canuse a wound prediction ML model (e.g., wound prediction ML model 124illustrated in FIGS. 1-2 ) to predict a care plan.

For example, the wound prediction service can use detected woundcharacteristics, generated using a wound detection service (e.g., thewound detection service 112 illustrated in FIG. 1 ) and a wounddetection ML model (e.g., the wound detection ML model 114 illustratedin FIGS. 1-2 ) from captured sensor data (e.g., a captured image of thewound). As discussed above, FIG. 8 provides an example of woundcharacteristics. The wound prediction service can further use any, orall, of patient characteristics (e.g., as illustrated in FIG. 9 ),patient medical history (e.g., as illustrated in FIG. 10 ), andhistorical wound care incidents (e.g., as illustrated in FIG. 11 ). Inan embodiment, the wound prediction service uses the historical woundcare data for ongoing training of the wound detection ML model.Alternatively, the wound prediction service does not receive thehistorical wound care data.

In an embodiment, the prediction controller 1310 transmits the predictedcare plan 1320 over a communication network 1330 to any, or all, of apatient 1340, a care provider 1350, and a healthcare facility 1360. Thecommunication network 1330 can be any suitable communication network,including the Internet, a wide area network, a local area network, or acellular network, and can use any suitable wired or wirelesscommunication technique (e.g., WiFi or cellular communication).

In an embodiment, any, or all, of the patient 1340, the care provider1350, and the healthcare facility 1360 receive the predicted care plan.The predicted care plan 1320 can then be used to treat the patientwound. For example, the patient 1340 can receive the predicted care plan1320 at a suitable electronic device (e.g., a smartphone, tablet, laptopcomputer, desktop computer, or any other suitable device) and can use itfor treatment (e.g., using a mobile application or local applicationrunning on the patient device, or accessing the predicted care plan 1320over the communication network 1330).

Similarly, the care provider 1350 or the healthcare facility 1360 (e.g.,a healthcare professional at the healthcare facility 1360) can receivethe predicted care plan 1320. In an embodiment, any, or all, of patient1340, the care provider 1350, and the healthcare facility 1360 store thepredicted care plan 1320. For example, this can allow the recipient toaccess the predicted care plan 1320 without requiring a continuousnetwork connection.

FIG. 14 depicts ongoing monitoring of patient care for wound managementand treatment using computer vision, according to one embodiment. Asdiscussed above in relation to the ongoing patient monitoring data 170illustrated in FIG. 1 , in an embodiment the patient care plan can berevised based on ongoing monitoring of the treatment progress for thepatient's wound. In an embodiment, a patient is treated at an outpatientfacility 1430. The outpatient facility 1430 continues to monitortreatment of the wound.

For example, the patient, or a care provider, can continue to captureelectronic images of the wound as it is treated, or capture electronicsensor data during treatment. The patient, or care provider, cantransmit this outpatient monitoring data 1432 (e.g., the captured imageor other sensor data) to a prediction controller 1410 (e.g., theprediction controller 200 illustrated in FIG. 2 ) using a communicationnetwork 1420. The communication network 1420 can be any suitablecommunication network, including the Internet, a wide area network, alocal area network, or a cellular network, and can use any suitablewired or wireless communication technique (e.g., WiFi or cellularcommunication).

In an embodiment, the prediction controller 1410 can use the outpatientmonitoring data 1432 to revise the predicted care plan. For example, asdiscussed above in relation to FIG. 4 , a computer vision service canuse a computer vision ML model to identify wound characteristics from acaptured wound image. These wound characteristics can then be used topredict a wound care plan using a care plan prediction ML model (e.g.,as discussed above in relation to FIG. 7 ). The outpatient monitoringdata 1432 can include one or more additional captured images of thewound, and a suitable computer vision ML model can be used to detectwound characteristics from these images. The prediction controller 1410can then use the updated wound characteristics to predict an updatedwound care plan.

Alternatively, or in addition, the patient is treated at healthcarefacility 1440. Just like at the outpatient facility 1430, the patient'swound can be continuously monitored at the healthcare facility 1440(e.g., by a care provider or by the patient). The care provider, orpatient, can transmit facility monitoring data 1442 (e.g., updatedcaptured sensor data for the wound to the prediction controller 1410using the communication network 1420. The prediction controller 1410 canuse the facility monitoring data 1442 to revise the predicted care plan.For example, as discussed above in relation to FIG. 4 , a computervision service can use a computer vision ML model to identify woundcharacteristics from a captured wound image. These wound characteristicscan then be used to predict a wound care plan using a care planprediction ML model (e.g., as discussed above in relation to FIG. 7 ).The facility monitoring data 1442 can include one or more additionalcaptured images of the wound, and a suitable computer vision ML modelcan be used to detect wound characteristics from these images. Theprediction controller 1410 can then use the updated woundcharacteristics to predict an updated wound care plan.

Further, in an embodiment, the outpatient monitoring data 1432 and thefacility monitoring data 1442 can be used to continuously train the careplan prediction ML model. For example, outpatient monitoring data 1432and the facility monitoring data 1442 can include additional capturedimages of the wound during treatment. The computer vision service can beused to identify characteristics of these wounds, and the prediction MLmodel can identify, from these characteristics, how treatment isprogressing for the patient. This indication of progress, along with thepreviously predicted care plan, can be used as training data to furtherrefine the care plan prediction ML model.

Example Clauses

Implementation examples are described in the following numbered clauses:

Clause 1: A method, comprising: determining a plurality ofcharacteristics of a wound for a patient based on an image of the wound,comprising: detecting the plurality of characteristics based onanalyzing the image using a first machine learning (ML) model trained todetect wound characteristics from a captured image, identifying patientmedical data comprising a plurality of characteristics relating to amedical history for the patient, and predicting a first care plan forthe patient based on providing the plurality of characteristics of thewound and the patient medical data to a second ML model, wherein thesecond ML model is trained to predict the first care plan using priorwound care outcome data comprising a plurality of prior wound careoutcomes relating to a plurality of prior patients, and wherein thefirst care plan is configured to be used to treat the wound for thepatient.

Clause 2: The method of any of clauses 1 or 3-10, further comprising:determining a second plurality of characteristics of the wound for thepatient based on analyzing a second image of the wound, captured duringtreatment of the wound relating to the predicted first care plan, usingthe first ML model, and predicting a second care plan for the patientbased on providing the second plurality of characteristics of the woundto the second ML model.

Clause 3: The method of any of clauses 1-2 or 4-10, wherein the secondplurality of characteristics of the wound is further used to modify thesecond ML model through further training based on the second pluralityof characteristics and the first care plan.

Clause 4: The method of any of clauses 1-3 or 5-10, further comprising:identifying a prophylactic treatment task for the wound based on atleast one of the plurality of characteristics of the wound or the careplan, and transmitting an electronic alert relating to the treatmenttask.

Clause 5: The method of any of clauses 1-4 or 6-10, wherein identifyingthe prophylactic treatment task is based on the at least one of theplurality of characteristics of the wound and is identified using thesecond ML model, further comprising: transmitting the alert to a careprovider for the patient electronically using a communication network,prior to completing the predicting the care plan for the patient.

Clause 6: The method of any of clauses 1-5 or 7-10, wherein detectingthe plurality of characteristics of the wound further comprises:determining at least one of a depth, a color, or a size of the wound,based on the image of the wound.

Clause 7: The method of any of clauses 1-6 or 8-10, wherein the priorwound care outcome data comprises data reflecting wound characteristics,treatment, and resolution for each of a plurality of past woundsrelating to the plurality of prior patients.

Clause 8: The method of any of clauses 1-7 or 9-10, wherein the careplan comprises one or more recommended treatment tasks for the wound,comprising at least one of: a medication, a patient action, or a careprovider action to treat the wound.

Clause 9: The method of any of clauses 1-8 or 10, further comprising:modifying treatment of the wound based on the one or more recommendedtreatment tasks.

Clause 10: The method of any of clauses 1-9, further comprising:identifying additional data captured by a sensor during treatment orassessment of the wound, wherein predicting the first care plan isfurther based on providing the identified additional data to the secondML model.

Clause 11: A processing system, comprising: a memory comprisingcomputer-executable instructions; and one or more processors configuredto execute the computer-executable instructions and cause the processingsystem to perform a method in accordance with any one of Clauses 1-10.

Clause 12: A processing system, comprising means for performing a methodin accordance with any one of Clauses 1-10.

Clause 13: A non-transitory computer-readable medium comprisingcomputer-executable instructions that, when executed by one or moreprocessors of a processing system, cause the processing system toperform a method in accordance with any one of Clauses 1-10.

Clause 14: A computer program product embodied on a computer-readablestorage medium comprising code for performing a method in accordancewith any one of Clauses 1-10.

Additional Considerations

The preceding description is provided to enable any person skilled inthe art to practice the various embodiments described herein. Theexamples discussed herein are not limiting of the scope, applicability,or embodiments set forth in the claims. Various modifications to theseembodiments will be readily apparent to those skilled in the art, andthe generic principles defined herein may be applied to otherembodiments. For example, changes may be made in the function andarrangement of elements discussed without departing from the scope ofthe disclosure. Various examples may omit, substitute, or add variousprocedures or components as appropriate. For instance, the methodsdescribed may be performed in an order different from that described,and various steps may be added, omitted, or combined. Also, featuresdescribed with respect to some examples may be combined in some otherexamples. For example, an apparatus may be implemented or a method maybe practiced using any number of the aspects set forth herein. Inaddition, the scope of the disclosure is intended to cover such anapparatus or method that is practiced using other structure,functionality, or structure and functionality in addition to, or otherthan, the various aspects of the disclosure set forth herein. It shouldbe understood that any aspect of the disclosure disclosed herein may beembodied by one or more elements of a claim.

As used herein, the word “exemplary” means “serving as an example,instance, or illustration.” Any aspect described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother aspects.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover a, b, c,a-b, a-c, b-c, and a-b-c, as well as any combination with multiples ofthe same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b,b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

The methods disclosed herein comprise one or more steps or actions forachieving the methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims. Further, thevarious operations of methods described above may be performed by anysuitable means capable of performing the corresponding functions. Themeans may include various hardware and/or software component(s) and/ormodule(s), including, but not limited to a circuit, an applicationspecific integrated circuit (ASIC), or processor. Generally, where thereare operations illustrated in figures, those operations may havecorresponding counterpart means-plus-function components with similarnumbering.

The following claims are not intended to be limited to the embodimentsshown herein, but are to be accorded the full scope consistent with thelanguage of the claims. Within a claim, reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. No claim element is tobe construed under the provisions of 35 U.S.C. § 112(f) unless theelement is expressly recited using the phrase “means for” or, in thecase of a method claim, the element is recited using the phrase “stepfor.” All structural and functional equivalents to the elements of thevarious aspects described throughout this disclosure that are known orlater come to be known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed by the claims. Moreover, nothing disclosed herein isintended to be dedicated to the public regardless of whether suchdisclosure is explicitly recited in the claims.

What is claimed is:
 1. A method, comprising: determining a plurality ofcharacteristics of a wound for a patient based on an image of the wound,comprising: detecting the plurality of characteristics based onanalyzing the image using a first machine learning (ML) model trained todetect wound characteristics from a captured image; identifying patientmedical data comprising a plurality of characteristics relating to amedical history for the patient; predicting a first care plan for thepatient based on providing the plurality of characteristics of the woundand the patient medical data to a second ML model, wherein the second MLmodel is trained to predict the first care plan using prior wound careoutcome data comprising a plurality of prior wound care outcomesrelating to a plurality of prior patients, and wherein the first careplan is configured to be used to treat the wound for the patient;identifying a prophylactic treatment task for the wound based on atleast one of the plurality of characteristics of the wound or the careplan; and transmitting an electronic alert relating to the treatmenttask.
 2. The method of claim 1, wherein identifying the prophylactictreatment task is based on the at least one of the plurality ofcharacteristics of the wound and is identified using the second MLmodel, further comprising: transmitting the alert to a care provider forthe patient electronically using a communication network, prior tocompleting the predicting the care plan for the patient.
 3. A method,comprising: determining a plurality of characteristics of a wound for apatient based on an image of the wound, comprising: detecting theplurality of characteristics based on analyzing the image using a firstmachine learning (ML) model trained to detect wound characteristics froma captured image; identifying patient medical data comprising aplurality of characteristics relating to a medical history for thepatient; predicting a first care plan for the patient based onproviding: (i) the plurality of characteristics of the wound, withoutthe image of the wound, and (ii) the patient medical data, to a secondML model, wherein the second ML model is trained to predict the firstcare plan using prior wound care outcome data comprising a plurality ofprior wound care outcomes relating to a plurality of prior patients, andwherein the first care plan is configured to be used to treat the woundfor the patient; determining a second plurality of characteristics ofthe wound for the patient based on analyzing a second image of thewound, captured during treatment of the wound relating to the predictedfirst care plan, using the first ML model; and modifying the second MLmodel through further training based on the second plurality ofcharacteristics and the first care plan.
 4. The method of claim 3,further comprising: predicting a second care plan for the patient basedon providing the second plurality of characteristics of the wound to thesecond ML model.
 5. The method of claim 3, further comprising:identifying a prophylactic treatment task for the wound based on atleast one of the plurality of characteristics of the wound or the careplan.
 6. The method of claim 3, wherein detecting the plurality ofcharacteristics of the wound further comprises: determining at least oneof a depth, a color, or a size of the wound, based on the image of thewound.
 7. The method of claim 3, wherein the prior wound care outcomedata comprises data reflecting wound characteristics, treatment, andresolution for each of a plurality of past wounds relating to theplurality of prior patients.
 8. The method of claim 3, furthercomprising: identifying additional data captured by a sensor duringtreatment or assessment of the wound, wherein predicting the first careplan is further based on providing the identified additional data to thesecond ML model.
 9. The method of claim 3, wherein the care plancomprises one or more recommended treatment tasks for the wound,comprising at least one of: a medication, a patient action, or a careprovider action to treat the wound.
 10. The method of claim 9, furthercomprising: modifying treatment of the wound based on the one or morerecommended treatment tasks.
 11. An apparatus comprising: a memory; anda hardware processor communicatively coupled to the memory, the hardwareprocessor configured to perform operations comprising: determining aplurality of characteristics of a wound for a patient based on an imageof the wound, comprising: detecting the plurality of characteristicsbased on analyzing the image using a first machine learning (ML) modeltrained to detect wound characteristics from a captured image;identifying patient medical data comprising a plurality ofcharacteristics relating to a medical history for the patient; andpredicting a first care plan for the patient based on providing: (i) theplurality of characteristics of the wound, without the image of thewound, and (ii) the patient medical data, to a second ML model, whereinthe second ML model is trained to predict the first care plan usingprior wound care outcome data comprising a plurality of prior wound careoutcomes relating to a plurality of prior patients, and wherein thefirst care plan is configured to be used to treat the wound for thepatient.
 12. The apparatus of claim 11, the operations furthercomprising: determining a second plurality of characteristics of thewound for the patient based on analyzing a second image of the wound,captured during treatment of the wound relating to the predicted firstcare plan, using the first ML model; and predicting a second care planfor the patient based on providing the second plurality ofcharacteristics of the wound to the second ML model.
 13. The apparatusof claim 12, wherein the second plurality of characteristics of thewound is further used to modify the second ML model through furthertraining based on the second plurality of characteristics and the firstcare plan.
 14. The apparatus of claim 11, the operations furthercomprising: identifying a prophylactic treatment task for the woundbased on at least one of the plurality of characteristics of the woundor the care plan; and transmitting an electronic alert relating to thetreatment task.
 15. The apparatus of claim 14, wherein identifying theprophylactic treatment task is based on the at least one of theplurality of characteristics of the wound and is identified using thesecond ML model, the operations further comprising: transmitting thealert to a care provider for the patient electronically using acommunication network, prior to completing the predicting the care planfor the patient.
 16. The apparatus of claim 11, wherein the prior woundcare outcome data comprises data reflecting wound characteristics,treatment, and resolution for each of a plurality of past woundsrelating to the plurality of prior patients.
 17. A non-transitorycomputer-readable medium comprising instructions that, when executed bya processor, cause the processor to perform operations comprising:determining a plurality of characteristics of a wound for a patientbased on an image of the wound, comprising: detecting the plurality ofcharacteristics based on analyzing the image using a first machinelearning (ML) model trained to detect wound characteristics from acaptured image; identifying patient medical data comprising a pluralityof characteristics relating to a medical history for the patient; andpredicting a first care plan for the patient based on providing: (i) theplurality of characteristics of the wound, without the image of thewound, and (ii) the patient medical data, to a second ML model, whereinthe second ML model is trained to predict the first care plan usingprior wound care outcome data comprising a plurality of prior wound careoutcomes relating to a plurality of prior patients, and wherein thefirst care plan is configured to be used to treat the wound for thepatient.
 18. The non-transitory computer-readable medium of claim 17,the operations further comprising: determining a second plurality ofcharacteristics of the wound for the patient based on analyzing a secondimage of the wound, captured during treatment of the wound relating tothe predicted first care plan, using the first ML model; and predictinga second care plan for the patient based on providing the secondplurality of characteristics of the wound to the second ML model,wherein the second plurality of characteristics of the wound is furtherused to modify the second ML model through further training based on thesecond plurality of characteristics and the first care plan.
 19. Thenon-transitory computer-readable medium of claim 17, the operationsfurther comprising: identifying a prophylactic treatment task for thewound based on the plurality of characteristics of the wound, using thesecond ML model; and transmitting an electronic alert to a care providerfor the patient electronically using a communication network, prior tocompleting the predicting the care plan for the patient.
 20. Thenon-transitory computer-readable medium of claim 17, wherein the priorwound care outcome data comprises data reflecting wound characteristics,treatment, and resolution for each of a plurality of past woundsrelating to the plurality of prior patients.